EyeC3D: 3D video eye tracking dataset

Despite efforts of the scientific community in recent years, little is known about the mechanisms of the human visual system that control visual attention when watching 3D content. To help understanding these mechanisms and develop more accurate visual attention models, we created a public 3D video eye tracking dataset. The dataset provides the eye tracking information corresponding to eight stereoscopic video sequences. The eye tracking information includes the fixation points and fixation density maps measured during subjective experiments. This paper describes the dataset in details, including the stereoscopic video sequences, the eye tracking experiments, and the computation of the fixation density maps.

Presented at:

6th International Workshop on Quality of Multimedia Experience (QoMEX), Singapore, September 18-20, 2014